Intelligent driver monitoring systems based on physiological sensor signals: A review

2013 ◽  
Author(s):  
Shahina Begum
Keyword(s):  
Monitoring Systems ◽  
Driver Monitoring ◽  
Sensor Signals ◽  
Physiological Sensor

scholarly journals Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3836
Author(s):  
António Lobo ◽  
Sara Ferreira ◽  
António Couto
Keyword(s):  
Short Distance ◽  
Monitoring Systems ◽  
Mobility Patterns ◽  
Long Distance ◽  
Road Crashes ◽  
Driving Time ◽  
Driver Monitoring ◽  
Driver Inattention ◽  
Ordered Probit Models ◽  
Breaking Time

Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.

scholarly journals A Physiological Sensor-Based Android Application Synchronized with a Driving Simulator for Driver Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 399 ◽  
Author(s):  
David González-Ortega ◽  
Francisco Díaz-Pernas ◽  
Mario Martínez-Zarzuela ◽  
Míriam Antón-Rodríguez
Keyword(s):  
Driving Simulator ◽  
Android Application ◽  
Game Engine ◽  
Vehicle Data ◽  
Driver Monitoring ◽  
Skin Response ◽  
Android App ◽  
Physiological Sensors ◽  
Electrocardiogram Ecg ◽  
Physiological Sensor

In this paper, we present an Android application to control and monitor the physiological sensors from the Shimmer platform and its synchronized working with a driving simulator. The Android app can monitor drivers and their parameters can be used to analyze the relation between their physiological states and driving performance. The app can configure, select, receive, process, represent graphically, and store the signals from electrocardiogram (ECG), electromyogram (EMG) and galvanic skin response (GSR) modules and accelerometers, a magnetometer and a gyroscope. The Android app is synchronized in two steps with a driving simulator that we previously developed using the Unity game engine to analyze driving security and efficiency. The Android app was tested with different sensors working simultaneously at various sampling rates and in different Android devices. We also tested the synchronized working of the driving simulator and the Android app with 25 people and analyzed the relation between data from the ECG, EMG, GSR, and gyroscope sensors and from the simulator. Among others, some significant correlations between a gyroscope-based feature calculated by the Android app and vehicle data and particular traffic offences were found. The Android app can be applied with minor adaptations to other different users such as patients with chronic diseases or athletes.

Average heart rate for driver monitoring systems

2016 ◽  
Vol 4 (3/4) ◽  
pp. 282
Author(s):  
J.M. Cooper ◽  
F. Biondi ◽  
D.L. Strayer ◽  
J.R. Coleman
Keyword(s):  
Heart Rate ◽  
Monitoring Systems ◽  
Average Heart Rate ◽  
Driver Monitoring

Face Pose Estimation From Rigid Face Landmarks For Driver Monitoring Systems

2017 ◽  
Vol 2017 (19) ◽  
pp. 83-88 ◽  
Author(s):  
Bhawani Shankar ◽  
Dakala Jayachandra ◽  
Kalyan Kumar Hati
Keyword(s):  
Pose Estimation ◽  
Monitoring Systems ◽  
Driver Monitoring

Time-To-Collision: A Sensitive Measure of Driver Interaction with Traffic in a Simulated Driving Task

1997 ◽  
Vol 41 (2) ◽  
pp. 1028-1031 ◽  
Author(s):  
Dary D. Fiorentino ◽  
Zareh Parseghian
Keyword(s):  
Real Time ◽  
Performance Criteria ◽  
Monitoring Systems ◽  
Blood Alcohol ◽  
Simulated Driving ◽  
Time To Collision ◽  
Driver Monitoring ◽  
Driving Task ◽  
Simulated Driving Task ◽  
Minimum Performance

In the future, on-board driver monitoring systems could use time-to-collision (TTC) metric algorithms as a real-time measure of driving performance, and alert the driver if performance falls below minimum performance criteria. Such monitoring systems remain years away, but it is currently possible to measure TTC in a simulator. This paper discusses a study to determine whether TTC varies as a function of driver impairment in a simulated driving task. Alcohol was administered to eleven participants, and TTC measures were obtained at 0.00%, 0.04% and 0.08% blood alcohol concentrations (BAC). The results support use of the median TTC, which varied as a function of BAC, as a measure of in-traffic maneuvering performance.

scholarly journals Driver Monitoring Systems: Perceived Fairness of Consequences when Distractions are Detected

2021 ◽  
Author(s):  
Michael A. Nees
Keyword(s):  
Perceived Risk ◽  
Online Survey ◽  
Driver Distraction ◽  
Monitoring Systems ◽  
Perceived Fairness ◽  
Automated Driving ◽  
Safety Feature ◽  
Driver Monitoring ◽  
Versus Level ◽  
Level 2

Driver monitoring may become a standard safety feature to discourage distraction in vehicles with or without automated driving functions. Research to date has focused on technology for identifying driver distraction—little is known about how drivers will respond to monitoring systems. An exploratory online survey assessed the perceived risk and reasonableness associated with driving distractions as well as the perceived fairness of potential consequences when a driver monitoring system detects distractions under either manual driving or Level 2 automated driving. Although more re- search is needed, results suggested: (1) fairness was associated with perceived risk; (2) alerts generally were viewed as fair; (3) more severe consequences (feature lockouts, insurance reporting, automation lockouts, involuntary takeovers) generally were viewed as less fair; (4) fairness ratings were similar for manual versus Level 2 driving, with some potential exceptions; and (5) perceived risk of distractions was slightly lower with automated driving.

Android Application to Monitor Physiological Sensor Signals Simultaneously

2016 ◽  
pp. 228-233
Author(s):  
David González-Ortega ◽  
Francisco Javier Díaz-Pernas ◽  
Amine Khadmaoui ◽  
Mario Martínez-Zarzuela ◽  
Míriam Antón-Rodríguez
Keyword(s):  
Android Application ◽  
Sensor Signals ◽  
Physiological Sensor
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